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The Department of Mechanical Engineering at Ohio University has designed, constructed, and controlled a new 6‐dof in‐parallel‐actuated platform, a combination and modification of existing designs. The 6‐PSU platform consists of six legs with a prismatic joint, spherical joint, and universal joint connecting links in each leg which move the platform in the six Cartesian freedoms with respect to the base. The prismatic joint is actuated while the other two joints in each leg are passive. The six prismatic joints move vertically with respect to the base, which appears to be a big improvement over the standard Gough/Stewart platform. Experimental results from the Ohio University manipulator are presented.

The paper aims to achieve translational shaking tests on a 6-DOF hydraulic parallel manipulator. Shaking tests are commonly performed on shaking tables, which are generally used for small motion ranges and are usually financially costly. The research is required to generate shaking motions in three translational directions for a specimen for shaking tests, but it also needs to produce 6-degree of freedom (DOF) motions with large motion ranges.

A hydraulic 6-DOF (degree of freedom) parallel manipulator is applied to achieve this goal. The link-space control is adopted for the manipulator, and PID controller and feed-forward controller are used for each loop of the system. A hybrid reference signal generator is proposed by using a shaking controller, which is developed to convert the shaking motion into position signal. The converted result is directly added to the pose signal. The whole real-time control system is realized by using MATLAB xPC Target.

The developed method is verified on the hydraulic 6-DOF parallel manipulator with specimen. Experiments show very promising results that the proposed technology is really applicable to perform translational shaking tests on the hydraulic parallel manipulator.

A simple yet efficient solution is proposed that allows shaking tests in three translational directions performed on the hydraulic 6-DOF parallel manipulator with wide motion ranges. The paper presents a state-of-the-art related to the applications of parallel robots in several fields of technology.

The precise control and dynamic analysis of the electrical Stewart platform have not been so well treated in the literature. This paper aims to design a novel model‐based controller to improve the tracing performance of the electrical Stewart platform. Moreover, the simulations under uncertain environments are used to verify the robustness of the controller.

In the electrical Stewart platform, there exist two special movements of the motor systems: motor systems' movement with the actuators and meanwhile the rotors and snails' rotation around their axis. The Kane equation is used to compute the driven torque of the movements of motor systems, actuators and movable platform. The improved dynamic models of the electrical Stewart platform which consider the motor systems and actuators' influences are used to design the novel controller. The PID controller and the simple model‐based controller are also developed to compare with the novel one. Moreover, the robustness of the controller is verified by the platform friction and the parameters uncertainty.

Simulation results show that the novel model‐based controller can gain a better tracing performance than the PID controller and even the simple model‐based controller. Under the environments of the platform with friction and 5% parameters variety, the tracing performance of the novel controller is also satisfactory, which verifies the robustness of the controller. Most importantly, the novel model‐based controller can be used in a higher precision control demand and a more complicated environment.

The main contribution of this paper is to derive a novel model‐based controller considering the motor systems' influence, which enhances the robustness of the controller. To the authors' best knowledge, such a framework for the improved model based controller has not been well treated in the past literature. The conventional PID controller and a simple model‐based controller are also built to verify the advantages of the improved model‐based controller.

The purpose of this paper is to present an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

Unlike the convention of developing a set of kinematic equations and then solving them, an alternative numerical algorithm is proposed in which the principal components of link lengths are used as a bridge to analyze the forward kinematics of a Stewart platform. The values of link lengths are firstly transformed to the values of principal components through principal component analysis. Then, the computation of the values of positional variables is transformed to a two‐dimensional nonlinear minimization problem by using the relationships between principal components and positional variables. A hybrid Nelder Mead‐particle swarm optimizer (NM‐PSO) algorithm and a modified NM algorithm are used to solve the two‐dimensional nonlinear minimization problem.

Simulation experiments have been conducted to validate the numerical algorithm and experimental results show that the numerical algorithm is valid and can achieve good accuracy and high efficiency.

This paper proposes an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

The purpose of this paper is to present an algorithm for inverse dynamics problem of a generally configured Stewart platform which is both fast and accurate.

A Newton‐Euler approach is presented, using the advantage of body coordinate frames, instead of inertial ones in order to omit redundant matrix transformations.

The method is found to lead to an efficient algorithm for inverse dynamics of a generally configured Stewart platform, which is at least three times faster than the available algorithms. This algorithm is at the same time more accurate, due to considering the gyroscopic effects of rotary parts within the legs.

Utilizing body coordinate frames for both platform and legs (instead of inertial ones) and taking into account the gyroscopic effects of the rotary parts within the leg, are the innovative aspects of this paper. The more significant achievement of the presented method is the remarkably faster rate of convergence, which is very important in feedback linearization control.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The purpose of this paper is to present systematic optimal design procedures for the Gough-Stewart platforms used as engineering motion simulators.

Three systematic optimal design procedures are proposed to solve the engineering design problems for the Gough-Stewart platform used as motion simulators. In these systematic optimal design procedures, two contradicting design optimality criteria with good representations of performances of the Gough-Stewart platforms are chosen as the objective functions. In addition, the two objective function optimization problems are solved by using the multi-objective evolutionary algorithms.

In the systematic optimal design procedures, multiple compromised design solutions are found by using Elitist Non-Dominated Sorting Genetic Algorithm version II in the primary design stage, and many candidates can be used in the secondary design stage for higher decisions. Two higher decision methods have been presented to choose the final solutions.

This paper proposes three systematic optimal design procedures to solve the practical design problems of the Gough-Stewart platforms used as motion simulators, which are very important for the engineering designers.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

In this study the design of motion‐based flight simulators is carried out by specifying the performance required of the motion cueing mechanism, to generate translational and angular motions as a 6–3 Stewart Platform Mechanism (SPM). These motions are intended to approximate the specific forces and angular accelerations encountered by the pilot in the simulated aircraft. Firstly, the dynamics of this 6–3 SPM is given in closed form as in our earlier study. Then, for the control of obtained dynamic model, a leg‐length based PD algorithm is applied. In the optimization of the applied PD algorithm's coefficients, Real Coded Genetic Algorithms are used. So as to have faster and effective system's performance, the fitness function chosen, in Genetic Algorithms, having maximum overshoot value, settling time and steady state error which are obtained from the unit step response. The performance of the system studied is compared to the similar studies in the literature exist.

This paper aims to combine and further develop different mathematical models of the workspace representation of 6 degrees of freedom parallel mechanisms and to bring a new point of view to existing workspace analysis methods through using neural networks (NN).

For the orientation workspace of the 6‐3 SPM, discretization method is used which is based on Euler angles and the NN algorithm is applied.

The workspace analysis is carried out in the direction perpendicular to the moving platform which is the most workable direction of 6‐3 Stewart platform mechanisms and NN algorithm has decreased processing time.

The determination of the point, on that direction, at which the workspace is maximum, is outlined. It is the first time that the NN is used for classification of workspace of a parallel manipulator.